A battery is composed of several electrical energy storage cells. These cells are electrically connected to one other between two electrical terminals of the battery.
A known way to determine a battery's state-of-charge includes, at each time k and for each cell of the battery, measuring, for each cell, the value yk of the voltage between terminals of that cell and the current ik of the charge or discharge current of that cell. Then, at least at certain of these times k, and for each cell, an electronic calculator estimates the state-of-charge, SOCk, of the cell as a function of the value yk and the current ik measured for that cell at that time k. The battery's overall state-of-charge can then be estimated from the states-of-charge estimated for each of its cells.
A cell's state-of-charge is not a physical quantity that is directly measurable. Thus, it must be estimated. Its estimation requires implementing an estimation algorithm. Such algorithms are non-trivial and require considerable computational power. For example, such estimation algorithms are described in part 3 of the following article: L. Plett, et al.: “Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs”, journal of Power Sources, 2004, page 252-292. Hereinafter, this article shall be denoted as “Plett 2004.”
Known methods thus estimate states-of-charge at each time k and then use those states to estimate an overall state-of-charge for the battery. These known methods work well. But they require a considerable computational resources.